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Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model

  • Bin LiEmail author
  • Shuai Ding
  • Guolei Song
  • Jiajia Li
  • Qian Zhang
Systems-Level Quality Improvement
  • 33 Downloads
Part of the following topical collections:
  1. Distributed Analytics and Deep Learning in Health Care

Abstract

The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical data. In order to achieve the regional medical and public health data analysis through artificial intelligence technologies, spark data analysis is adopted as the research platform for hypertension patients, and artificial intelligence technologies are used to preprocess the data with inconsistency, redundancy, incompleteness, noise and error; Aiming at the unbalanced data sets, the Z-score standard is adopted to convert data into usable form suitable for data mining. And, the application of Logistic, Naive Bayesian regression, and support vector machine based on three groups of different prognosis in severe cases, including stroke, heart failure and renal failure symptoms, establish the risk early warning model for 3 years time. In addition, to select the optimal feature subset based on medicine big-data features, the model simplification and optimization are done in training process, the experimental results show that the feature subset selection can ensure the classification performance similar to the clinical features of the model. Therefore, according to chronic cardiovascular disease, acute cardiovascular events and cardiovascular events caused by critical illness events, we screen out the relevant prognosis of serious illness (stroke, heart failure, renal failure), which is related to the prognosis of serious illness. Targeted prevention has a guiding role and practical significance according to the results of artificial intelligence analysis.

Keywords

Chronic cardiovascular disease Artificial Intelligence Z-score standard Logistic Naive Bayesian regression Support vector machine Clinical feature 

Notes

Acknowledgements

This study was funded by 2017 Social Science Key Project of Bengbu Medical College. (Fund no.: BYKY17146skZD).

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.

Human and animal rights

The paper does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Bin Li
    • 1
    • 2
    Email author
  • Shuai Ding
    • 2
  • Guolei Song
    • 1
  • Jiajia Li
    • 1
  • Qian Zhang
    • 1
  1. 1.The First Affiliated Hospital of Bengbu Medical CollegeBengbuChina
  2. 2.School of Management HeFei University of TechnologyHefeiChina

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